Distributed Robust Kalman Filtering with Unknown and Noisy Parameters in Sensor Networks
This paper investigates the distributed filtering for discrete-time-invariant systems in sensor networks where each sensor’s measuring system may not be observable, and each sensor can just obtain partial system parameters with unknown coefficients which are modeled by Gaussian white noises. A fully...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2018-01-01
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| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2018/7954263 |
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| _version_ | 1832565391263531008 |
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| author | Donghua Chen Ya Zhang Cheng-Lin Liu Yangyang Chen |
| author_facet | Donghua Chen Ya Zhang Cheng-Lin Liu Yangyang Chen |
| author_sort | Donghua Chen |
| collection | DOAJ |
| description | This paper investigates the distributed filtering for discrete-time-invariant systems in sensor networks where each sensor’s measuring system may not be observable, and each sensor can just obtain partial system parameters with unknown coefficients which are modeled by Gaussian white noises. A fully distributed robust Kalman filtering algorithm consisting of two parts is proposed. One is a consensus Kalman filter to estimate the system parameters. It is proved that the mean square estimation errors for the system parameters converge to zero if and only if, for any one system parameter, its accessible node subset is globally reachable. The other is a consensus robust Kalman filter to estimate the system state based on the system matrix estimations and covariances. It is proved that the mean square estimation error of each sensor is upper-bounded by the trace of its covariance. An explicit sufficient stability condition of the algorithm is further provided. A numerical simulation is given to illustrate the results. |
| format | Article |
| id | doaj-art-727c6a373a8441a68bfe376b7ce34a86 |
| institution | Kabale University |
| issn | 1026-0226 1607-887X |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-727c6a373a8441a68bfe376b7ce34a862025-02-03T01:07:54ZengWileyDiscrete Dynamics in Nature and Society1026-02261607-887X2018-01-01201810.1155/2018/79542637954263Distributed Robust Kalman Filtering with Unknown and Noisy Parameters in Sensor NetworksDonghua Chen0Ya Zhang1Cheng-Lin Liu2Yangyang Chen3School of Architecture Engineering, Nanjing Institute of Technology, Nanjing 211167, ChinaSchool of Automation, Southeast University, Nanjing 210096, ChinaInstitute of Automation, Jiangnan University, Wuxi 214122, ChinaSchool of Automation, Southeast University, Nanjing 210096, ChinaThis paper investigates the distributed filtering for discrete-time-invariant systems in sensor networks where each sensor’s measuring system may not be observable, and each sensor can just obtain partial system parameters with unknown coefficients which are modeled by Gaussian white noises. A fully distributed robust Kalman filtering algorithm consisting of two parts is proposed. One is a consensus Kalman filter to estimate the system parameters. It is proved that the mean square estimation errors for the system parameters converge to zero if and only if, for any one system parameter, its accessible node subset is globally reachable. The other is a consensus robust Kalman filter to estimate the system state based on the system matrix estimations and covariances. It is proved that the mean square estimation error of each sensor is upper-bounded by the trace of its covariance. An explicit sufficient stability condition of the algorithm is further provided. A numerical simulation is given to illustrate the results.http://dx.doi.org/10.1155/2018/7954263 |
| spellingShingle | Donghua Chen Ya Zhang Cheng-Lin Liu Yangyang Chen Distributed Robust Kalman Filtering with Unknown and Noisy Parameters in Sensor Networks Discrete Dynamics in Nature and Society |
| title | Distributed Robust Kalman Filtering with Unknown and Noisy Parameters in Sensor Networks |
| title_full | Distributed Robust Kalman Filtering with Unknown and Noisy Parameters in Sensor Networks |
| title_fullStr | Distributed Robust Kalman Filtering with Unknown and Noisy Parameters in Sensor Networks |
| title_full_unstemmed | Distributed Robust Kalman Filtering with Unknown and Noisy Parameters in Sensor Networks |
| title_short | Distributed Robust Kalman Filtering with Unknown and Noisy Parameters in Sensor Networks |
| title_sort | distributed robust kalman filtering with unknown and noisy parameters in sensor networks |
| url | http://dx.doi.org/10.1155/2018/7954263 |
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